The Power of Algorithmic Insights: Evaluating LinkedIn's Machine Learning Recommendations in Career Development

In the digital age, career development increasingly relies on online platforms, with LinkedIn emerging as a critical tool for professionals. This research examines the impact of machine learning-based recommendations on LinkedIn in shaping career development, utilizing the Technology Acceptance Model (TAM) framework. A structured questionnaire was distributed over four weeks to 434 LinkedIn users, collecting data on various factors including perceived usefulness, ease of use, and user attitudes. The results, analyzed through Smart PLS, indicate that algorithmic recommendations significantly enhance perceived usefulness and ease of use, driving user engagement and supporting career development. These findings highlight the strategic value of machine learning in professional development, offering insights into how digital platforms can be leveraged to optimize career growth. The findings of the study reveal that algorithmic recommendations significantly influence user behavior, leading to a positive effect on career development.